Since its inception, social media have been routinely data mined for marketing consumer goods. Starting around 2010, researchers began to realize that the same techniques could be used for influenza surveillance (Culotta
2010). Since then, social media analytics for public health
has been expanded to monitor a variety of conditions,
including cholera (Chunara, Andrews, and Brownstein
2012), mental health (Golder and Macy 2011), and diet
(Widener and Li 2014). This body of work has shown that
social media can be a useful complement to traditional methods, such as surveys of medical providers or individuals, for
gathering aggregate public health statistics. Our work
extends the social media analytics approach to a new
domain, foodborne illness. Our most important contribution, however, is that we go beyond simply monitoring pop-ulation-level prevalence. Our system, nEmesis, provides specific, actionable information, which is used to support
effective public health interventions.

n Foodborne illness afflicts 48 million
people annually in the US alone. More
than 128,000 are hospitalized and 3000
die from the infection. While preventable
with proper food safety practices, the traditional restaurant inspection process has
limited impact given the predictability and
low frequency of inspections, and the
dynamic nature of the kitchen environment. Despite this reality, the inspection
process has remained largely unchanged
for decades. CDC has even identified food
safety as one of seven ” winnable battles”;
however, progress to date has been limited.
In this work, we demonstrate significant
improvements in food safety by marrying
AI and the standard inspection process.
We apply machine learning to Twitter
data, develop a system that automatically
detects venues likely to pose a public
health hazard, and demonstrate its efficacy in the Las Vegas metropolitan area in a
double-blind experiment conducted over
three months in collaboration with Nevada’s health department. By contrast, previous research in this domain has been limited to indirect correlative validation using
only aggregate statistics. We show that the
adaptive inspection process is 64 percent
more effective at identifying problematic
venues than the current state of the art. If
fully deployed, our approach could prevent
more than 9000 cases of foodborne illness
and 557 hospitalizations annually in Las
Vegas alone. Additionally, adaptive
inspections result in unexpected benefits,
including the identification of venues lacking permits, contagious kitchen staff, and
fewer customer complaints filed with the
Las Vegas health department.